BIA–MS–MS: biomolecular interaction analysis for functional proteomics

One of the experimental processes of functional proteomics is the analysis of protein interaction. Here, we review a new analytical platform, BIA–MS, for protein interaction analysis. BIA–MS is an integration of a surface plasmon resonance biosensor for real-time interaction analysis and mass spectrometry for the subsequent identification of interacting molecules.     

Definitive screening design enables optimization of LC–ESI–MS/MS parameters in proteomics

In proteomics, more than 100,000 peptides are generated from the digestion of human cell lysates. Proteome samples have a broad dynamic range in protein abundance; therefore, it is critical to optimize various parameters of LC–ESI–MS/MS to comprehensively identify these peptides. However, there are many parameters for LC–ESI–MS/MS analysis. In this study, we applied definitive screening design to simultaneously optimize 14 parameters in the operation of monolithic capillary LC–ESI–MS/MS to increase the number of identified proteins and/or the average peak area of MS1. The simultaneous optimization enabled the determination of two-factor interactions between LC and MS. Finally, we found two parameter sets of monolithic capillary LC–ESI–MS/MS that increased the number of identified proteins by 8.1% or the average peak area of MS1 by 67%. The definitive screening design would be highly useful for high-throughput analysis of the best parameter set in LC–ESI–MS/MS systems.     

Ion mobility–mass spectrometry for structural proteomics

Ion mobility coupled to mass spectrometry has been an important tool in the fields of chemical physics and analytical chemistry for decades, but its potential for interrogating the structure of proteins and multiprotein complexes has only recently begun to be realized. Today, ion mobility–mass spectrometry is often applied to the structural elucidation of protein assemblies that have failed high-throughput crystallization or NMR spectroscopy screens. Here, we highlight the technology, approaches and data that have led to this dramatic shift in use, including emerging trends such as the integration of ion mobility–mass spectrometry data with more classical (e.g., ‘bottom-up’) proteomics approaches for the rapid structural characterization of protein networks.     

ModuleSearch: finding functional modules in a protein–protein interaction network

Many biological processes are performed by a group of proteins rather than by individual proteins. Proteins involved in the same biological process often form a densely connected sub-graph in a protein–protein interaction network. Therefore, finding a dense sub-graph provides useful information to predict the function or protein complex of uncharacterised proteins in the sub-graph. We developed a heuristic algorithm that finds functional modules in a protein–protein interaction network and visualises the modules. The algorithm has been implemented in a platform-independent, standalone program called ModuleSearch. In an interaction network of yeast proteins, ModuleSearch found 366 overlapping modules. Of the modules, 71% have a function shared by more than half the proteins in the module and 58% have a function shared by all proteins in the module. Comparison of ModuleSearch with other programs shows that ModuleSearch finds more sub-graphs than most other programs, yet a higher proportion of the sub-graphs correspond to known functional modules. ModuleSearch and sample data are freely available to academics at http://bclab.inha.ac.kr/ModuleSearch.     

Investigation of an on-line two-dimensional chromatographic approach for peptide analysis in plasma by LC–MS–MS

Reversed phase and hydrophilic interaction chromatography (HILIC) were successfully coupled for the on-line extraction and quantitative analysis of peptides by ESI–LC–MS/MS. A total of 11 peptides were utilized to determine the conditions for proper focusing and separation on both dimensions. Minor modifications to the initial organic composition of the first reversed-phase dimension provided options between a comprehensive (generic) or more selective approach for peptide transfer to the second HILIC dimension. Ion-pairing with trifluoroacetic acid (TFA) provided adequate chromatographic retention and peak symmetry for the selected peptides on both C₁₈ and HILIC. The resulting signal suppression from TFA was partially recovered by a post-column “TFA fix” using acetic acid yielding improvements in sensitivity. Minimal sample preparation aligned with standard on-line extraction procedures provided highly reproducible and robust results for over 300 sequential matrix injections. Final optimized conditions were successfully employed for the quantitation of peptide PTHrP (1–36) in rat K₃EDTA plasma from 25.0 to 10,000 ng/mL using PTHrP (1–34) as the analog internal standard. This highly orthogonal two-dimensional configuration was found to provide the unique selectivity required to overcome issues with interfering endogenous components and minimize electrospray ionization effects in biological samples.     

Optimized GC–MS metabolomics for the analysis of kidney tissue metabolites

Introduction

Metabolomics is a promising approach for discovery of relevant biomarkers in cells, tissues, organs, and biofluids for disease identification and prediction. The field has mostly relied on blood-based biofluids (serum, plasma, urine) as non-invasive sources of samples as surrogates of tissue or organ-specific conditions. However, the tissue specificity of metabolites pose challenges in translating blood metabolic profiles to organ-specific pathophysiological changes, and require further downstream analysis of the metabolites.

Objectives

As part of this project, we aim to develop and optimize an efficient extraction protocol for the analysis of kidney tissue metabolites representative of key primate metabolic pathways.

Methods

Kidney cortex and medulla tissues of a baboon were homogenized and extracted using eight different extraction protocols including methanol/water, dichloromethane/methanol, pure methanol, pure water, water/methanol/chloroform, methanol/chloroform, methanol/acetonitrile/water, and acetonitrile/isopropanol/water. The extracts were analyzed by a two-dimensional gas chromatography time-of-flight mass-spectrometer (2D GC–ToF-MS) platform after methoximation and silylation.

Results

Our analysis quantified 110 shared metabolites in kidney cortex and medulla tissues from hundreds of metabolites found among the eight different solvent extractions spanning low to high polarities. The results revealed that medulla is metabolically richer compared to the cortex. Dichloromethane and methanol mixture (3:1) yielded highest number of metabolites across both the tissue types. Depending on the metabolites of interest, tissue type, and the biological question, different solvents can be used to extract specific groups of metabolites.

Conclusion

This investigation provides insights into selection of extraction solvents for detection of classes of metabolites in renal cortex and medulla, which is fundamentally important for identification of prognostic and diagnostic metabolic kidney biomarkers for future therapeutic applications.

     

Stable deuterium internal standard for the isotope-dilution LC–MS/MS analysis of elastin degradation

Chemical synthesis of the deuterium isotope desmosine-d₄ has been achieved. This isotopic compound possesses all four deuterium atoms at the alkanyl carbons of the alkyl amino acid substitution in the desmosine molecule and is stable toward acid hydrolysis; this is required in the measurement of two crosslinking molecules, desmosine and isodesmosine, as biomarkers of elastic tissue degradation. The degradation of elastin occurs in several widely prevalent diseases. The synthesized desmosine-d₄ is used as the internal standard to develop an accurate and sensitive isotope-dilution liquid chromatography–tandem mass spectrometry analysis, which can serve as a generalized method for an accurate analysis of desmosine and isodesmosine as biomarkers in many types of biological tissues involving elastin degradation.     

Sub-proteomic fractionation,iTRAQ, and OFFGEL-LC–MS/MS approaches to cardiac proteomics

Using an in solution based approach with a sub-proteomic fraction enriched in cardiac sarcomeric proteins; we identified protein abundance in ischemic and non-ischemic regions of rat hearts stressed by acute myocardial ischemia by ligating the left-anterior descending coronary artery in vivo for 1 h without reperfusion. Sub-cellular fractionation permitted more in depth analysis of the proteome by reducing the sample complexity. A series of differential centrifugations produced nuclear, mitochondrial, cytoplasmic, microsomal, and sarcomeric enriched fractions of ischemic and non-ischemic tissues. The sarcomeric enriched fractions were labeled with isobaric tags for relative quantitation (iTRAQ), and then fractionated with an Agilent 3100 OFFGEL fractionator. The OFFGEL fractions were run on a Dionex U-3000 nano LC coupled to a ThermoFinnigan LTQ running in PQD (pulsed Q dissociation) mode. The peptides were analyzed using two search engines MASCOT (MatrixScience), and MassMatrix with false discovery rate of < 5%. Compared to no fractionation prior to LC–MS/MS, fractionation with OFFGEL improved the identification of proteins approximately four-fold. We found that approximately 22 unique proteins in the sarcomeric enriched fraction had changed at least 20%. Our workflow provides an approach for discovery of unique biomarkers or changes in the protein profile of tissue in disorders of the heart.     

Protein–protein interaction networks improve the proteomics data interpretation in induced apoptosis

Evaluation of: Deighton RF, Kerr LE, Short DM et al. Network generation enhances interpretation of proteomics data from induced apoptosis. Proteomics DOI: 10.1002/pmic.200900112 (2010) (Epub ahead of print).

The huge ongoing improvements in proteomics technologies, including the development of high-throughput mass spectrometry, are resulting in ever increasing information on protein behavior during cellular processes. The exponential accumulation of proteomics data has the promise to advance biomedical sciences by shedding light on the most important events that regulate mammalian cells under normal and pathophysiological conditions. This may provide practical insights that will impact medical practice and therapy, and may permit the development of a new generation of personalized therapeutics. Proteomics, as a powerful tool, creates numerous opportunities as well as challenges. At the different stages, data interpretation requires proteomics analysis, various tools to help deal with large proteomics data banks and the extraction of more functional information. Network analysis tools facilitate proteomics data interpretation and predict protein functions, functional interactions and in silica identification of intracellular pathways. The work reported by Deighton and colleagues illustrates an example of improving proteomics data interpretation by network generation. The authors used ingenuity pathway analysis to generate a protein network predicting direct and indirect interaction between 13 proteins found to be affected by staurosporine treatment. Importantly, the authors highlight the caution required when interpreting the results from a small number of proteins analyzed using network analysis tools.     

DESI–MS as a tool for direct lipid analysis in cultured cells

Desorption electrospray ionization may be used as a fast and convenient method for analysis and identification of lipids in the cell culture. Oxidative stress, which usually involves changes in lipids, was used as a model of pathology to show the utility of this analysis methodology. This paper addresses the surface preparation of cell culture slides, induction of oxidative stress, and cell monolayer culture preparation as well as optimization of the analysis. Advantages and drawbacks of the method were also discussed.     

2DDB – a bioinformatics solution for analysis of quantitative proteomics data

Background  

We present 2DDB, a bioinformatics solution for storage, integration and analysis of quantitative proteomics data. As the data complexity and the rate with which it is produced increases in the proteomics field, the need for flexible analysis software increases.     

Urinary amino acid analysis: A comparison of iTRAQ®–LC–MS/MS,GC–MS,and amino acid analyzer

Urinary amino acid analysis is typically done by cation-exchange chromatography followed by post-column derivatization with ninhydrin and UV detection. This method lacks throughput and specificity. Two recently introduced stable isotope ratio mass spectrometric methods promise to overcome those shortcomings. Using two blinded sets of urine replicates and a certified amino acid standard, we compared the precision and accuracy of gas chromatography/mass spectrometry (GC–MS) and liquid chromatography–tandem mass spectrometry (LC–MS/MS) of propyl chloroformate and iTRAQ^® derivatized amino acids, respectively, to conventional amino acid analysis. The GC–MS method builds on the direct derivatization of amino acids in diluted urine with propyl chloroformate, GC separation and mass spectrometric quantification of derivatives using stable isotope labeled standards. The LC–MS/MS method requires prior urinary protein precipitation followed by labeling of urinary and standard amino acids with iTRAQ^® tags containing different cleavable reporter ions distinguishable by MS/MS fragmentation. Means and standard deviations of percent technical error (%TE) computed for 20 amino acids determined by amino acid analyzer, GC–MS, and iTRAQ^®–LC–MS/MS analyses of 33 duplicate and triplicate urine specimens were 7.27 ± 5.22, 21.18 ± 10.94, and 18.34 ± 14.67, respectively. Corresponding values for 13 amino acids determined in a second batch of 144 urine specimens measured in duplicate or triplicate were 8.39 ± 5.35, 6.23 ± 3.84, and 35.37 ± 29.42. Both GC–MS and iTRAQ^®–LC–MS/MS are suited for high-throughput amino acid analysis, with the former offering at present higher reproducibility and completely automated sample pretreatment, while the latter covers more amino acids and related amines.     

An anisotropic constitutive model for immersogeometric fluid–structure interaction analysis of bioprosthetic heart valves

This paper considers an anisotropic hyperelastic soft tissue model, originally proposed for native valve tissue and referred to herein as the Lee–Sacks model, in an isogeometric thin shell analysis framework that can be readily combined with immersogeometric fluid–structure interaction (FSI) analysis for high-fidelity simulations of bioprosthetic heart valves (BHVs) interacting with blood flow. We find that the Lee–Sacks model is well-suited to reproduce the anisotropic stress–strain behavior of the cross-linked bovine pericardial tissues that are commonly used in BHVs. An automated procedure for parameter selection leads to an instance of the Lee–Sacks model that matches biaxial stress–strain data from the literature more closely, over a wider range of strains, than other soft tissue models. The relative simplicity of the Lee–Sacks model is attractive for computationally-demanding applications such as FSI analysis and we use the model to demonstrate how the presence and direction of material anisotropy affect the FSI dynamics of BHV leaflets.     

Protein–protein interaction network of the marine microalga Tetraselmis subcordiformis: prediction and application for starch metabolism analysis

Under stressful conditions, the non-model marine microalga Tetraselmis subcordiformis can accumulate a substantial amount of starch, making it a potential feedstock for the production of fuel ethanol. Investigating the interactions of the enzymes and the regulatory factors involved in starch metabolism will provide potential genetic manipulation targets for optimising the starch productivity of T. subcordiformis. For this reason, the proteome of T. subcordiformis was utilised to predict the first protein–protein interaction (PPI) network for this marine alga based on orthologous interactions, mainly from the general PPI repositories. Different methods were introduced to evaluate the credibility of the predicted interactome, including the confidence value of each PPI pair and Pfam-based and subcellular location-based enrichment analysis. Functional subnetworks analysis suggested that the two enzymes involved in starch metabolism, starch phosphorylase and trehalose-phosphate synthase may be the potential ideal genetic engineering targets.     

Metabolomics meets functional assays: coupling LC–MS and microfluidic cell-based receptor-ligand analyses

Introduction

Metabolomics has become a valuable tool in many research areas. However, generating metabolomics-based biochemical profiles without any related bioactivity is only of indirect value in understanding a biological process. Therefore, metabolomics research could greatly benefit from tools that directly determine the bioactivity of the detected compounds.

Objective

We aimed to combine LC–MS metabolomics with a cell based receptor assay. This combination could increase the understanding of biological processes and may provide novel opportunities for functional metabolomics.

Methods

We developed a flow through biosensor with human cells expressing both the TRPV1, a calcium ion channel which responds to capsaicin, and the fluorescent intracellular calcium ion reporter, YC3.6. We have analysed three contrasting Capsicum varieties. Two were selected with contrasting degrees of spiciness for characterization by HPLC coupled to high mass resolution MS. Subsequently, the biosensor was then used to link individual pepper compounds with TRPV1 activity.

Results

Among the compounds in the crude pepper fruit extracts, we confirmed capsaicin and also identified both nordihydrocapsaicin and dihydrocapsaicin as true agonists of the TRPV1 receptor. Furthermore, the biosensor was able to detect receptor activity in extracts of both Capsicum fruits as well as a commercial product. Sensitivity of the biosensor to this commercial product was similar to the sensory threshold of a human sensory panel.

Conclusion

Our results demonstrate that the TRPV1 biosensor is suitable for detecting bioactive metabolites. Novel opportunities may lie in the development of a continuous functional assay, where the biosensor is directly coupled to the LC–MS.

     

Erratum to: An in silico biomechanical analysis of the stent–esophagus interaction

Biomechanics and Modeling in Mechanobiology - In the original publication of the article, Tables&nbsp;2 and 3 were published with error. The correct tables are provided below (Tables 2, 3). The...     

A new modified Edman procedure for analysis of N-terminal valine adducts in hemoglobin by LC–MS/MS

A rapid and sensitive method using liquid chromatography–tandem mass spectrometry (LC–MS/MS) for simultaneous determination of adducts from acrylamide, glycidamide and ethylene oxide to N-terminal valines in hemoglobin (Hb) was developed. This new procedure is based on the same principles as the N-alkyl Edman procedure for analysis of adducts from electrophilic agents to N-terminal valines in Hb. The N-substituted valines can be detached, enriched and measured selectively as thiohydantoins by the use of an Edman reagent, in this case fluorescein isothiocyanate (FITC). This procedure is denoted as the “adduct FIRE procedure” as the FITC reagent is used for measurement of adducts (R) formed from electrophilic compounds with a modified Edman procedure. In this study, fluorescein thiohydantoin (FTH) analytes of N-substituted valines from acrylamide, glycidamide and ethylene oxide, as well as their corresponding hepta- and tri-deuterium-substituted analogues, were synthesized. These analytes (n = 8) were then characterized by LC–MS/MS (ESI, positive ion mode) and obtained product ions were interpreted. A considerable work with optimization of the FIRE procedure™, resulted in a procedure in which low background levels of the studied adducts could be measured from 250 μL lyzed whole blood samples (human non-smokers). The analytes were enriched and purified with solid phase extraction columns and analyzed by LC–MS/MS with LOQ down to 1 pmol adduct/g Hb. Compared to other procedures for determination of N-terminal Hb adducts, the introduction of FITC has led to a simplified procedure, where whole blood also can be used, giving new opportunities and reduced hand on time with increased sample throughput.     

Network cluster analysis of protein–protein interaction network identified biomarker for early onset colorectal cancer

Colorectal cancer (CRC) is a major cause of morbidity and mortality throughout the world. However, the genetic alterations and molecular mechanism of the early onset CRCs are not fully investigated. The present study aimed to characterize early onset CRC by analyzing its gene expression compared with normal controls and to identify network-based biomarkers of early onset CRC. The gene expression profiles of early onset CRC were downloaded from Gene Expression Omnibus and the differentially expressed genes (DEGs) in CRC patients were identified. Then, a protein–protein interaction (PPI) network was constructed and the clusters in PPI were analyzed by ClusterONE. Furthermore, the gene ontology functional analysis and pathway enrichment analysis were conducted to the modules in PPI network. A systems biology approach integrating microarray data and PPI was further applied to construct a PPI network in CRC. Total 631 DEGs were identified from the early onset CRC compared to healthy controls. These genes were found to be involved in several biological processes, including cell communication, cell proliferation, cell shape and apoptosis. Five functional modules which may play important roles in the initiation of early onset CRC were identified from the PPI network. Functional annotation revealed that these five modules were involved in the pathways of signal transduction, carcinogenesis and metastasis. The hub nodes of these five modules, CDC42, TEX11, QKI, CAV1 and FN1, may serve as the biomarkers of early onset CRC and have the potential to be targets for therapeutic intervention. However, further investigations are still needed to confirm our findings.